To receive full credit, you’ll need to deliver on all of the items in the checklist below. Please read carefully through this checklist before you make your project proposal. You are (within these checklist constraints) strongly urged to limit scope and make the necessary simplifying assumptions so that you can deliver your work on time!
Goal: My objective in this project is seeking to understand the relationship between population growth and the crime rates in the United States between the year 1994 to 2018 and to comment and conclude on the overal pattern.
To analyze the crime rates in the US for the period from 1994 to 2013, we will use the data set provided by the FBI. See link: https://ucr.fbi.gov/crime-in-the-u.s/2013/crime-in-the-u.s.-2013/tables/1tabledatadecoverviewpdf/table_1_crime_in_the_united_states_by_volume_and_rate_per_100000_inhabitants_1994-2013.xls
such as the OSEMN workflow or Hadley Wickham’s Grammar of Data Science. [Example: First the data is acquired, then necessary transformations and clean-up are performed, then the analysis and presentation work is performed]
I downloaded data for the period 1994 - 2018 and saved it as a .csv file in my local drive and later uploaded it into my github directory for accessibility.
library(ggplot2)
library(XML)
library(RCurl)
library(knitr)
library(dplyr)
library(plyr)
library(tidyr)
library(plotly)
library(tidyverse)
library(tidyselect)
library(data.table)
library(readxl)
library(fBasics)#Reading the dataset from my Github repository
crime_data <- read_csv("https://raw.githubusercontent.com/igukusamuel/DATA-607-Final-Project/master/CrimeData.csv")
#Observing the dataset
#glimpse(crime_data)
#str(crime_data)
#summary(crime_data)
#Confirm data was loaded correctly
head(crime_data, 2)## # A tibble: 2 x 24
## Year Population Violent_crime Violent_crime_r~ Murder_and_nonn~
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1994 260327021 1857670 714. 23326
## 2 1995 262803276 1798792 684. 21606
## # ... with 19 more variables:
## # Murder_and_nonnegligent_manslaughter_rate <dbl>, Rape <dbl>,
## # Rape_rate <dbl>, Robbery <dbl>, Robbery_rate <dbl>,
## # Aggravated_assault <dbl>, Aggravated_assault_rate <dbl>,
## # Property_crime <dbl>, Property_crime_rate <dbl>, Burglary <dbl>,
## # Burglary_rate <dbl>, Larceny_theft <dbl>, Larceny_theft_rate <dbl>,
## # Motor_vehicle_theft <dbl>, Motor_vehicle_theft_rate <dbl>, X21 <lgl>,
## # `_` <lgl>, X23 <lgl>, X24 <lgl>(e.g., two or more of these: relational or CSV, Neo4J, web page [scraped or API], MongoDB, etc.)
[Examples: transforming from wide to long; converting columns to date format]
#Select columns relevant to our analysis [1:20 only].
#Note Column 21:24 are empty
crime_data <- crime_data[, 1:20]
# Add a column on % Crime Rate (Total crimes / Total Population)
crime_data <- crime_data %>% mutate(PercentageCrimeRate = (
Violent_crime + Murder_and_nonnegligent_manslaughter +
Rape + Robbery + Aggravated_assault + Larceny_theft +
Property_crime + Burglary +
Motor_vehicle_theft) / Population * 100
)
# Add a column on Average Crime Rate (Total crimes / N (no of crime classifications))
crime_data %>% mutate(AverageCrimeRate = (
Violent_crime_rate + Murder_and_nonnegligent_manslaughter_rate +
Rape_rate + Robbery_rate + Aggravated_assault_rate + Larceny_theft_rate +
Property_crime_rate + Burglary_rate +
Motor_vehicle_theft_rate) / 9
) -> crime_data
# Print out all column names to confirm the two just added
names(crime_data)## [1] "Year"
## [2] "Population"
## [3] "Violent_crime"
## [4] "Violent_crime_rate"
## [5] "Murder_and_nonnegligent_manslaughter"
## [6] "Murder_and_nonnegligent_manslaughter_rate"
## [7] "Rape"
## [8] "Rape_rate"
## [9] "Robbery"
## [10] "Robbery_rate"
## [11] "Aggravated_assault"
## [12] "Aggravated_assault_rate"
## [13] "Property_crime"
## [14] "Property_crime_rate"
## [15] "Burglary"
## [16] "Burglary_rate"
## [17] "Larceny_theft"
## [18] "Larceny_theft_rate"
## [19] "Motor_vehicle_theft"
## [20] "Motor_vehicle_theft_rate"
## [21] "PercentageCrimeRate"
## [22] "AverageCrimeRate"populationGrowth <-
ggplot(crime_data, aes(Year, Population, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Population Size Between 1994 - 2018") +
xlab("Years") + ylab("Population") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(populationGrowth)ViolentCrimeRate <-
ggplot(crime_data, aes(Year, Violent_crime_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Violent crime rate per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Violent_crime") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(ViolentCrimeRate)Murder_and_nonnegligent_manslaughter_rate <-
ggplot(crime_data, aes(Year, Murder_and_nonnegligent_manslaughter_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Rape Rate per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Rate_rate") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Murder_and_nonnegligent_manslaughter_rate)Rape_rate <-
ggplot(crime_data, aes(Year, Rape_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Rape Rate per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Rate_rate") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Rape_rate)Robbery_rate <-
ggplot(crime_data, aes(Year, Robbery_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Robberly Rate per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Robberly_Rate") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Robbery_rate)Aggravated_assault_rate <-
ggplot(crime_data, aes(Year, Aggravated_assault_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Aggrevated_Assualt per 100,000 growth between 1994 - 2018") +
xlab("Years") + ylab("Aggrevated_Assualt") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Aggravated_assault_rate)The Aggrevated_Assualt per 100,000 has been decreasing over the years between 1994 - 2014 with a small increment between 2014 to 2016. There after the rate decrease between 2016-2018.
Property_crime_rate <-
ggplot(crime_data, aes(Year, Property_crime_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Property Crime per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Property_crime") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Property_crime_rate)Burglary_rate <-
ggplot(crime_data, aes(Year, Burglary_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Burglary Rate per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Burglary_Rate") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Burglary_rate)Larceny_theft_rate <-
ggplot(crime_data, aes(Year, Larceny_theft_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Larcency theft per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Larcency_theft") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Larceny_theft_rate)Motor_vehicle_theft_rate <-
ggplot(crime_data, aes(Year, Motor_vehicle_theft_rate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Motor_vehicle_theft per 100,000 between 1994 - 2018") +
xlab("Years") + ylab("Violent_crime") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(Motor_vehicle_theft_rate)AverageCrimeRate <-
ggplot(crime_data, aes(Year, AverageCrimeRate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("Average Crime Rate between 1994 - 2018") +
xlab("Years") + ylab("Violent_crime") +
theme(
plot.title = element_text(color="blue", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="blue", size=15, face="bold")
)
ggplotly(AverageCrimeRate)PercentageCrimeRate <-
ggplot(crime_data, aes(Year, PercentageCrimeRate, group = 1)) +
geom_line(linetype = "dashed", color = "red") +
geom_point()+
ggtitle("% Crime Rate between 1994 - 2018") +
xlab("Year") + ylab("Overall % Crime Rate") +
theme(
plot.title = element_text(color="red", size=15, face="bold.italic"),
axis.text.x = element_text(angle=60, hjust=1),
axis.title.x = element_text(color="blue", size=15, face="bold"),
axis.title.y = element_text(color="orange", size=15, face="bold")
)
ggplotly(PercentageCrimeRate)The analysis above leads us to the following conclusions: The average rate of crime has decreased steadily over the period between 2014 and 2018.
There are many examples: I used ggmap; I created a decision tree; I ranked the results; I created my presentation slides directly from R; I figured out to use OAuth 2.0.
I created my presentation slides directly from R.
Was the presentation delivered in the allotted time (3 to 5 minutes)? # YES
Did you show (at least) one challenge you encountered in code and/or data, and what you did when you encountered that challenge? # YES
If you didn’t encounter any challenges, your assignment was clearly too easy for you!
Did the audience come away with a clear understanding of your motivation for undertaking the project? # YES
Did the audience come away with a clear understanding of at least one insight you gained or conclusion you reached or hypothesis you confirmed (rejected or failed to reject)? # YES
Have you delivered the submitted code and data where it is self-contained preferably in rpubs.com and github? # YES
Am I able to fully reproduce your results with what you’ve delivered? # YES
You won’t receive full credit if your code references data on your local machine!
Does all of the delivered code run without errors? # YES
Have you delivered your code and conclusions using a reproducible research tool such as RMarkdown? # YES
Were your draft project proposal, project, and presentation delivered on time? # YES
Any part of the project that is turned in late will receive a maximum grade of 80%. Please turn in your work on time! You are of course welcome to deliver ahead of schedule!